Accurate 3D pose estimation from a single depth image

Mao Ye, Xianwang Wang, Ruigang Yang, Liu Ren, Marc Pollefeys

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

205 Scopus citations

Abstract

This paper presents a novel system to estimate body pose configuration from a single depth map. It combines both pose detection and pose refinement. The input depth map is matched with a set of pre-captured motion exemplars to generate a body configuration estimation, as well as semantic labeling of the input point cloud. The initial estimation is then refined by directly fitting the body configuration with the observation (e.g., the input depth). In addition to the new system architecture, our other contributions include modifying a point cloud smoothing technique to deal with very noisy input depth maps, a point cloud alignment and pose search algorithm that is view-independent and efficient. Experiments on a public dataset show that our approach achieves significantly higher accuracy than previous state-of-art methods.

Original languageEnglish
Title of host publication2011 International Conference on Computer Vision, ICCV 2011
Pages731-738
Number of pages8
DOIs
StatePublished - 2011
Event2011 IEEE International Conference on Computer Vision, ICCV 2011 - Barcelona, Spain
Duration: Nov 6 2011Nov 13 2011

Publication series

NameProceedings of the IEEE International Conference on Computer Vision

Conference

Conference2011 IEEE International Conference on Computer Vision, ICCV 2011
Country/TerritorySpain
CityBarcelona
Period11/6/1111/13/11

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

Fingerprint

Dive into the research topics of 'Accurate 3D pose estimation from a single depth image'. Together they form a unique fingerprint.

Cite this